Although innovation generally provides measurable improvements in disease characteristics and patient survival, some benefits can remain unclear. This study aimed to investigate patient and healthcare provider (HCP) preferences for the innovative attributes of multiple myeloma (MM) treatments.
Methods
A cross-sectional, web-based, discrete choice experiment (DCE) survey was conducted among 200 patients with MM and 30 HCPs of patients with MM in the USA. A literature review, followed by interviews with patients with MM and HCPs, was undertaken to select five attributes (progression-free survival [PFS], chance of severe side effects, how patients live with MM treatments, scientific innovation, and monthly out-of-pocket [OOP] cost) and their levels. A Bayesian efficient design was used to generate DCE choice sets. Each choice set comprised two hypothetical MM treatment alternatives described by the selected attributes and their levels. Each patient and HCP was asked to choose a preferred alternative from each of the 11 choice sets. Mixed logit and latent class models were developed to estimate patient and HCP preferences for the treatment attributes.
Results
Overall, patients and HCPs preferred increased PFS, less chance of severe side effects, a treatment that offered life without treatment, scientific innovation, and lower OOP cost. From patients’ perspectives, PFS had the highest conditional relative importance (44.7%), followed by how patients live with MM treatments (21.6%) and scientific innovation (16.0%).
Conclusions
In addition to PFS, patients and HCPs also valued innovative MM treatments that allowed them to live without treatments and/or offered scientific innovation. These attributes should be considered when evaluating MM treatments.
Traditional health value assessment frameworks often overlook the value of innovation, risking the underestimation of treatments’ true benefits.
This study is the first to conduct a discrete choice experiment to investigate the preferences of patients with multiple myeloma (MM) and healthcare providers (HCPs) toward innovative MM treatment attributes.
In addition to PFS, patients with MM and HCP also valued two innovative MM treatment attributes, i.e., “living a life without treatments” and “scientific innovation.”
1 Introduction
Value in healthcare can be defined as a combination of what patients value and the interests of relevant stakeholders, such as payers and policymakers in the system [1]. Maximizing health outcomes or societal welfare with limited healthcare resources has become very challenging owing to the increasing healthcare expenditures that come with the advanced, newer group of interventions and the demand for evidence-based medicine and transparent reimbursement [2].
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Instead of measuring value by focusing on traditional outcomes such as quality-adjusted life years, value assessment frameworks are gradually incorporating additional elements that were historically disregarded when assessing the value of healthcare technologies and interventions [3]. The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Special Task Force identified various novel elements of value, such as the value of hope, real option value, equity, and scientific spillovers that should be considered when assessing the value of healthcare to inform decision-making [4]. Specifically, the ISPOR Special Task Force suggested that scientific spillover is an underdeveloped element and more research is needed to measure and incorporate this element.
Healthcare innovation is defined by the World Health Organization as the development of “new or improved health policies, systems, products and technologies, and services and delivery methods that improve people’s health, with a special focus on the needs of vulnerable populations” [5]. Healthcare innovation plays a critical role in maximizing health outcomes or societal welfare with limited resources and should be valued and incentivized for future healthcare development. Recently, studies advancing methods to measure and reward healthcare innovation have emerged [6]. However, these studies primarily focused only on the real option value of health technologies, the value of flexible vaccine manufacturing capacity, and the value of healthcare interventions beyond the healthcare sector. In a recent systematic review, eight innovation attributes were identified for healthcare technologies: novelty, step change, substantial benefits, improvement over existing technologies, convenience and/or adherence, added value, acceptable cost, and uncounted benefits [7]. However, the value of these innovation attributes was not rigorously assessed [8].
In the last two decades, innovation has led to significant improvements in treatments for multiple myeloma (MM), including immunomodulatory agents, proteasome inhibitors, and monoclonal antibodies, which further lead to improved survival outcomes [9‐11]. A study including patients with newly diagnosed MM, who were seen in an academic medical institute in the USA between 2004 and 2017, grouped them into three periods to assess survival trends over time. The 4-year survival estimates for the periods 2004–2007, 2008–2012, and 2013–2017 were 50%, 62%, and 75%, respectively [12]. However, most patients eventually relapsed or became refractory, and the disease remained incurable [13, 14].
The emergence of novel therapies has dramatically improved patient outcomes and changed the treatment landscape for MM; for example, chimeric antigen receptor T cell therapy has recently become available as one of the promising novel immunotherapeutic treatments for patients with refractory/relapsed MM (RRMM). Novel therapies prolong the survival and remission period of patients, with the potential to revolutionize the treatment landscape. However, there has been a lack of quantification of the value ascribed to the innovation of these treatments. Additionally, a recent study showed discrepancies in patients’ and physicians’ perceptions with respect to RRMM treatment decision-making [15]. However, shared decision-making is essential, given the various available treatments and the challenges in determining optimal care for patients with RRMM. Therefore, the objective of this study was to determine patient and healthcare provider (HCP) preferences for innovative MM treatment attributes. A discrete choice experiment (DCE) was chosen as a method in this study because it allows for the quantification of preferences by presenting participants with hypothetical treatment scenarios that reflect real-world trade-offs, making it well suited for capturing the complexity of shared decision-making in RRMM.
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2 Methods
A cross-sectional, web-based DCE survey was used in this study. The study design and reporting of this DCE were based on the guidance of a DCE user’s guide and two reports from the ISPOR Good Research Practices for Conjoint Analysis Task Force [16‐18]. The University of Utah Institutional Review Board (IRB) approved this study (IRB-00147477) to be exempt from human subject review.
2.1 Identification and Selection of Attributes and Levels
First, a systematic literature review (SLR) was conducted to develop a list of MM treatment attributes (i.e., efficacy, safety, administration and/or convenience, innovation, and cost) that are likely important to patients with MM and HCPs, and their levels. This list was used to guide two semistructured focus group discussions (FGDs) with ten English-speaking patients with MM and in-depth interviews with five HCPs involved in MM care between October and November 2022 to determine important MM treatment attributes. Of the ten patients participating in the FGDs, two were African American, one was Asian, and the others were white, and two were newly diagnosed and aged below 50 years. For the in-depth interviews, HCPs, who were involved in the patients’ decision-making processes, namely physician–oncologist, nurse practitioner, and clinical pharmacist, and had a minimum of 1 year of experience providing MM care, were included. From these qualitative approaches, two innovation attributes of MM treatments, including the opportunity to live without MM treatment and scientific innovation, were consistently identified by both patient and HCP groups. At the end of the interviews, patients and HCPs were asked to rank the importance of the discussed attributes. Finally, the study team members, consisting of clinicians, DCE experts, and health outcomes researchers, reviewed the evidence in the SLR and interviews, as well as the latest clinical trials and emerging therapies. Through various rounds of discussions and consensus meetings, the study team systematically assessed the relevance and clinical significance, and primarily based the decisions on the findings from the patient interviews to ensure that the selected attributes were potentially important to patients with MM. The final list of five attributes, with distinct levels, was developed through this approach (Table 1): (1) progression-free survival (PFS; 6, 12, 24, 48 months), (2) chance of severe side effects (5%, 10%, 20%), (3) how patients live with MM treatments (living without treatment; living with treatment at home; living with treatment at a hospital/clinic), (4) scientific innovation, defined as a treatment that either is first-in-class or leads to the development of better MM treatments (yes, no), and (5) monthly out-of-pocket (OOP) cost ($100, $500, $1000, $1500). The attribute levels of PFS, chance of severe side effects, and OOP cost were derived from the SLR findings, while the levels of the other two attributes, i.e., how patients live with MM treatments and scientific innovation, were obtained from the results of the focus group interviews with the patients, followed by the discussion among the study team members, including an MM specialist physician.
Table 1.
Treatment attributes and levels included in the discrete choice experiment
Attribute
Level
Progression-free survival
6, 12, 24, and 48 months
Chance of severe side effects
5%, 10%, and 20%
How patients live their life with treatments
Living without treatment, living with treatment at home, living with treatment at hospital or clinic
Scientific innovation
Yes, no
Out-of-pocket cost (per month)
$100, $500, $1000, and $1500
A treatment that provides the opportunity to live without treatment and/or offers scientific innovation was deemed innovative
2.2 Survey Development
A DCE was utilized to estimate the value that patients and HCPs placed on the attributes and associated attribute levels of MM treatments. Following the selection of attributes and their levels, a full-length survey was developed, incorporating a D-efficient DCE survey design created using Ngene® software (ChoiceMetrics, version 1.2). The design included six blocks, each including six choice sets, resulting in a total of 36 profiles. Each block contributed to one unique version of the survey. Each choice set included two unlabeled hypothetical MM treatment alternatives described by the selected attributes and their levels. In each choice set, participants were requested to choose a preferred alternative, followed by an option to opt out of the treatment. Before participants responded to DCE questions, detailed descriptions in plain language and graphics of the attributes, as well as practice questions, were provided to ensure their understanding and minimize bias. To ensure the quality of responses, one validity choice set, consisting of a repeated choice set, was included in each survey version, resulting in seven choice sets per patient. The content of the survey was validated by a clinical expert and a survey expert. Then, the survey was validated with five patients using the think-aloud method [19], with each session lasting approximately 1 h. During these sessions, patients were instructed to think out loud while completing the survey. On the basis of their feedback, the survey was modified and then piloted with 30 patients with MM. This pilot study was designed to further explore results and confirm whether the survey questions and choice experiment performed as intended [19]. It also generated prior parameters to inform a Bayesian-efficient design with four blocks, each including nine DCE choice sets in the main survey. In the main survey, two validity choice sets, consisting of dominant and repeated choice sets, were included in each survey version to ensure the quality of responses, resulting in 11 choice sets per patient or HCP (an example choice set is presented in Fig. 1). Questions about the demographic characteristics of patients or HCPs were included in the surveys. Questions on patients’ experience with MM (e.g., relapse experience, stem cell transplant experience) or HCPs’ experience treating patients with MM (e.g., the number of patients with MM seen per week) were also included in the surveys.
Fig. 1
Example choice set. Patients and HCPs were asked to choose the preferred alternative from a set of two hypothetical MM treatment options. Each alternative was described by a selected attribute and their levels to analyze patient and HCP preferences. HCP healthcare provider, MM multiple myeloma
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2.3 Inclusion Criteria and Data Collection
To be included in the patient survey, the following eligibility criteria were required: (1) diagnosed with MM by a physician, (2) aged 18 years and above, (3) proficient in English, and (4) residing in the USA. To be included in the HCP survey, an individual must be a physician, pharmacist, or nurse in the USA who provided care for patients with MM and was involved in and influenced patients’ treatment decisions. Survey participants were recruited between March and August 2023 by Global Perspectives (GP), a research firm based in the UK, using online panels, patient databases, social media, and advertisements. While a convenience sampling technique was used, GP was asked to ensure a good representation of US patients as reported in a national database such as the Surveillance, Epidemiology, and End Results database in terms of sex, race/ethnicity, insurance status, and geographical region. HCPs were purposely selected to include physicians, nurses, and pharmacists. By Orme’s rule of thumb, the minimum sample size of patients required for this study is 91 [20]. Additionally, upon consideration of the estimated sample size and other strategies, which included the ISPOR good research practice report [21] and a published practical guide [22], we targeted to include a minimum of 160 patients with MM for the patient survey. Owing to limited resources, this study targeted 30 HCPs, which should be sufficient on the basis of the research team’s previous DCE experience in treatments for another type of oncology disease state [23]. Both patient and HCP surveys were self-administered on a web-based platform. Each participant was randomized to one of the four survey versions.
2.4 Data Analysis
Only responses from the participants who passed the validity choice sets were included in the analysis. Patients’ and HCPs’ sociodemographic characteristics and experiences with MM were descriptively analyzed. Summary statistics, including mean and standard deviation (SD), or median and interquartile range (IQR), are reported for continuous variables. For categorical variables, frequencies and percentages are reported. On the basis of random utility theory, responses for each choice set were analyzed using a mixed logit (ML) model to determine the perceived value of an alternative with the utility function (Unsj) for participant n with a choice set s and an alternative j, where only main effects were included. All variables were assumed to be categorical, and effect coding was used:
where β0 is the constant reflecting the preferences for choosing the two treatment alternatives, instead of the opt-out alternative. βn1, βn2, βn3,…. βn11 are the coefficients or preference weights of the effect codes of the PFS, the chance of severe side effects (SSE), how patients live their life with treatments (LWT), having scientific innovation (SI), and the OOP cost per month (Cost), respectively. εnsj is the error term. The means and SDs of all preference weights were estimated using the maximum simulated likelihood method with 1000 Halton draws. A Wald test was used to evaluate whether there was a difference between the preference weights of two adjacent levels of the attributes. The estimated preference weights were then used to calculate the conditional relative importance of attributes. First, the range of attribute-specific levels was calculated by measuring the difference between the highest and lowest preference weights for the levels of the respective attribute. The conditional relative importance was then calculated by dividing the range of the attribute-specific levels by the sum of all attribute level ranges. All analyses were performed separately for patients’ and HCPs’ survey data. Additionally, a sensitivity analysis was performed including both valid and invalid responses.
Given the potential of preference heterogeneity across patients, a latent class model was developed to classify patients into distinct subgroups (or latent classes), each having a similar pattern of preferences for MM treatments. Up to six classes were fit to determine the optimal number of latent classes with the main effects. The model with the minimum Bayesian information criterion values was selected as the final latent class model. P value less than 0.05 was considered statistically significant. All analyses were performed using STATA version 17.0 (StataCorp 2021, College Station, TX, USA).
3 Results
Among 2149 patients with MM invited by GP, 285 (13.3%) patients agreed to participate, and 200 (9.3%) patients completed the survey. For the HCP survey, 931 HCPs were invited, 203 responded to participate (21.8%), and 30 (3.2%) completed the survey.
The characteristics of the 200 patients with MM are presented (Table S1 of the Electronic Supplementary Material [ESM]), and patients had a mean age of 63.8 years (SD: 8.4 years). Approximately half of the patients were male (n = 109, 54.5%), and the majority were white (n = 126, 63.0%) and had an education of a college degree or higher (n = 118, 59.0%). The median time since MM diagnosis was 4 years (IQR: 3–7 years). A higher percentage of patients had experienced relapses (n = 111, 55.5%), and the median number of prior lines of therapy was 3 (IQR: 3–4). The majority of patients had private (n = 64, 32.0%) or Medicare (n = 88, 44.0%) insurance coverage. Fatigue (n = 123, 61.5%), diarrhea (n = 85, 42.5%), nausea (n = 69, 34.5%), and peripheral neuropathy (n = 41, 20.5%) were reported to be the top four treatment-related side effects patients experienced from their current MM treatments. A total of 17 patients failed at least one of the two validity tests of the survey.
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The characteristics of the 30 HCPs are reported (Table S2 of the ESM). A total of 15 physicians, 9 nurses, and 6 pharmacists completed the survey. The mean age of HCPs was 51.3 years (SD: 12.8 years). Most HCPs were male (n = 19, 63.3%). The median years of providing care to patients with MM was 17 years (IQR: 11–27 years). The majority of HCPs provided care to ≥ 10 patients with MM per week (n = 19, 63.3%) and worked in the hemato-oncology department (n = 24, 80.0%). A total of six HCPs failed at least one of the two validity tests of the survey.
Figure 2 shows the patient (Fig. 2a) and HCP (Fig. 2b) preferences from the ML models. Likelihood ratio tests affirmed good model fit with log likelihoods of − 1381.9 for patient and − 237.3 for HCP (both P < 0.001). Table S3 of the ESM, which gives the parameter estimates from the ML model for patients, shows that the P value of the constant (P = 0.44) was not statistically significant, which aligns with the survey design, where alternatives are unlabeled and provide no information beyond the specified attributes. Both patients and HCPs preferred a treatment that offers longer PFS, less chance of severe side effects (5% versus 10%, and 10% versus 20%), the opportunity to live a life without treatment, scientific innovation, and OOP cost of $1000 relative to $1500 (Tables S3 and S4 of the ESM). Specifically, from the patient perspective for the innovation attribute “how patients live with MM treatments,” the preference weight was significantly greater for a treatment that provides the opportunity of living without treatment than for options of living with treatment at home (P ≤ 0.001). Similarly, the preference weight was significantly higher for a treatment that provides the opportunity of living with treatment at home than living with treatment at the hospital/clinic (P ≤ 0.001). Additionally, from both patient and HCP perspectives for another innovation attribute “scientific innovation,” the preference weight was significantly higher for a treatment with scientific innovation than for a treatment without scientific innovation (P ≤ 0.001 and 0.015, respectively). On the other hand, from the HCP perspective, there was no significant difference between the preference weights of living a life without treatment and with treatment at home, but they were significantly different for living a life with treatment at home compared with living a life with treatment at the hospital.
Fig. 2
Preference weights by mixed logit model for a patients and b healthcare providers. The change in utility associated with a change in the levels of each attribute is represented by the vertical distance between the preference weights for any two levels of that attribute. Larger differences between preference weights indicate that respondents viewed the change as having a relatively greater effect on overall utility. *Indicates significant difference between two levels of an attribute. The vertical bars surrounding each mean preference weight denote the 95% confidence interval
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For PFS, the preference weights of all adjacent levels were significantly different from the patient perspective, but the preference weights of PFS at 24 and 48 months were not significantly different from the HCP perspective. For both patients and HCPs, the preference weights of severe side effects at 10.0% and 20.0%, and OOP at $1000 and $1500 were significantly different. The results of the sensitivity analyses after including the responses from the participants who failed the validity tests were consistent with these findings (Table S4 of the ESM).
Figure 3 shows the conditional relative importance of each attribute. From both patient and HCP perspectives, PFS had the highest conditional relative importance (patients: 44.7%; HCPs: 30.6%), followed by how patients live with MM treatments (patients: 21.6%; HCPs: 21.8%) and scientific innovation (patients: 16.0%; HCPs: 19.4%). Preference heterogeneity was found among patients with MM and among HCPs, as indicated by the significant SDs of the preference weights of all attributes from the ML results (Tables S3 and S5 of the ESM).
Fig. 3
Conditional relative importance for a patients and b healthcare providers. The conditional relative importance is the difference between the preference weights on the most influential attribute and the least influential attribute. These differences are summed across attributes, and the sum is scaled to 100. The conditional importance of each attribute is a percentage of this total. PFS progression-free survival
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Results from the latent class analysis in patients with MM are reported in Fig. 4 and Table S6 of the ESM. Two latent classes were identified; class I included 61.7% and class II included 38.3% of the patients. The average age of the patients in class I and II was 63.3 and 65.2 years, respectively. There were more African American patients in class I and more Hispanic patients in class II. A significant difference in annual household income was found between the classes. Of class II members, 63.8% (n = 44) had an annual household income between $50,000 and $99,999, while 31.9% (n = 36) of class I members fell into this income bracket. A significantly higher proportion of patients in class II experienced relapses compared with those in class I. Similar patterns of preference for MM treatments were observed between the two latent classes. Both class I and class II preferred a treatment that offers longer PFS, less chance of severe side effects, the opportunity to live a life without treatment, scientific innovation, and lower OOP cost (Table S7 of the ESM). However, the two latent classes showed different conditional relative importance rankings for the five attributes. In class I, the attribute of how patients live with MM treatments had the highest conditional relative importance (27.5%), followed by the OOP cost (23.0%) and PFS (22.1%) (Fig. S1a of the ESM). In class II, PFS had the highest conditional relative importance (45.7%), followed by how they live with MM treatments (21.6%), and scientific innovation (16.8%) (Fig. S1b of the ESM).
Fig. 4
Preference weights for latent class models a class I and b class II. The change in utility associated with a change in the levels of each attribute is represented by the vertical distance between the preference weights for any two levels of that attribute. Larger differences between preference weights indicate that respondents viewed the change as having a relatively greater effect on overall utility. *Indicates significant difference between two levels of an attribute. The vertical bars surrounding each mean preference weight denote the 95% confidence interval
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4 Discussion
This study investigated the preference-based value of innovation of MM treatments from patient and HCP perspectives. While the distributions of patients’ sex and race/ethnicity in this study were similar to those from national data, the age, educational attainment, and insurance status differed [24]: the study population was slightly younger and had higher percentages of private insurance coverage. Additionally, patients in this study tended to have higher educational attainment and annual household income. HCPs’ median years of MM experience, patients’ median number of previous regimens, and their ability to comprehend attributes in the survey reflected that they understood MM treatments and should respond to the survey well. Unlike most stated preference studies focused on patients with RRMM [25‐27], a diverse MM patient population was included in this study regarding sociodemographic characteristics and different stages of the disease. This enabled us to understand the preferences of the broader patient population with MM.
Overall, the directions of the preference weights of all attributes except severe side effects and cost were intuitive, and there were significant differences across the preference weights of most attributes’ adjacent levels. Both patients and HCPs preferred an MM treatment that offers longer PFS, less chance of severe side effects, the opportunity to live a life without treatment, and scientific innovation. However, from the HCP perspective, the preference weights of PFS at 24 and 48 months were not significantly different, and there was no significant difference between living without treatment and living with treatment at home. One of the reasons could be that HCPs might perceive that any treatments with a PFS that is higher than 24 months would outperform the median PFS of the existing standard treatments for patients with RRMM [28, 29] and reach their highest expectation. It was also possible that this study included only a small number of HCPs and did not have enough power to detect the different significance of PFS between 24 and 48 months and between living with treatment at home and living without treatment. On the other hand, the preference weights of OOP cost were significantly different only at the attribute level for $1000 and $1500 from both patient and HCP perspectives. Additionally, from both patient and HCP perspectives, the preference weights of severe side effects were significantly different only at the attribute level of 10% and 20%. In other words, both patients and HCPs could tolerate or feel indifferent to the chance of severe side effects of up to 10% and the OOP cost of up to $1000. However, a higher chance of severe side effects and higher OOP cost than these levels would significantly lower the utilities of the treatments.
The two innovation attributes, viz. how patients live with MM treatments and scientific innovation, were considered the second and third most important attributes out of five from both patient and HCP perspectives. Although advances in MM treatments have changed MM into a manageable chronic disease, they come at the expense of prolonged, intensive, and intermittent treatment periods, which resulted in a high treatment burden [30, 31]. The burden interfered with work life and social participation in young patients, while, in older patients, the treatment burden compounded other comorbidities, leading to more dependency on caregivers [30]. Thus, living without treatments is intuitively invaluable for patients with MM, especially given the incurable nature of the disease. On the other hand, it was not unexpected that scientific innovation, defined as a treatment either first-in-class or leading to the development of better MM treatments in this study, was preferable to both patients and physicians. This concept of scientific innovation could be applicable and translatable to other disease areas, such as advances in precision medicine with gene and cell therapies for cancers or autoimmune diseases, and the mRNA-based COVID-19 vaccines that stood as impactful outcomes of scientific innovation, wherein the novelty of mRNA vaccines led to a breakthrough in tackling the pandemic [32].
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However, the overall patient preferences seemed heterogeneous. On the basis of the latent class analysis, we identified two latent classes that significantly differed by race/ethnicity, relapse status, and annual income level. The conditional relative importance of the preference weights of class I (younger, more African American patients, fewer relapses, the majority with annual income between $50,000 and $99,999) members was more uniform, i.e., all attributes are of almost equal importance. However, for class II (older, fewer African American patients, more relapses, the majority with annual income between $50,000 and $99,999), PFS was the most important attribute, and the chance of severe side effects was the least important. They also valued innovative MM treatments higher than patients in class I. Every attribute in this study tended to be important for patients with fewer relapses or those who did not have much MM treatment experience, while prolonged PFS tended to be the ultimate treatment goal for patients who had more relapse experience, without placing much weight on chance of severe side effects. In other words, our findings reflected that treatment choice in MM was complex, and, in the era of precision medicine, it is important to consider patients’ heterogeneity of treatment preferences in clinical practice to support a personalized approach to choosing treatment options.
The findings of this study have two important clinical and policy implications. First, HCPs may consider a patient-centered approach by incorporating patient preferences identified from this study in making MM treatment decisions with the patients. Second, patient preferences in obtaining innovative MM treatments should be considered by payers when developing their formularies. For example, incorporating innovative MM treatments that offer the opportunity to live without treatments and offer scientific innovation may allow patients better access to their preferred treatments. Ultimately, incorporating patient preferences in clinical practice and formularies may lead to improved patient compliance and outcomes. Furthermore, the inclusion of providers, namely physicians, pharmacists, and nurses, unlocks their preferred attributes of innovative MM treatments and brings greater context to help with shared decisions between patients and providers to ensure optimal patient care.
There are some limitations to be considered. First, like all stated preference studies, this DCE study might be subject to hypothetical bias. Reported preference might differ from revealed preference, given that choices were unlabeled and hypothetical. Second, only five attributes with predetermined categories were selected and included in this study; however, other attributes related to MM treatments might exist and influence participants’ treatment choices. It is noteworthy that this study mitigated these biases by conducting an SLR and qualitative interviews with patients and HCPs to select the study attributes and their corresponding categories from real-world practice and including an opt-out alternative to resemble real-world choice. Third, this study used a self-administered DCE survey to elicit preferences, which might be a source of response bias due to misinterpretation of the attributes. However, detailed plain language descriptions and graphics of the attributes, as well as practice questions, were provided to participants prior to the collection of their DCE responses. Also, approximately one-fifth of the HCPs did not pass the validity tests. While their data were excluded from the analysis, these failures might reflect challenges in the HCPs’ survey comprehension or engagement, which should be improved in future studies. Finally, the perceived overlap among PFS, living a life without treatment, and scientific innovation could be a limitation of this study. While initial concerns were raised regarding the distinctiveness of these attributes, interviews with patients with MM and HCPs confirmed that these attributes were, in fact, discrete and should be treated separately. However, it was possible that some patients and HCPs in this study might still perceive overlaps, which could influence how these attributes were interpreted and prioritized. As a result, future research is needed to examine this possible overlap.
5 Conclusions
This study elicited patient and HCP preferences for MM treatments and was the first to demonstrate how patients valued the innovation attributes of MM treatments, which has not been captured in the traditional value assessment methods such as cost-effectiveness analysis. However, significant heterogeneity of patient preference for these innovation attributes existed. The potential for considering the value of innovation as an additional element in the value assessment of MM treatments needs to be examined further.
Acknowledgments
The study was supported by Bristol Myers Squibb. The authors received editorial and writing support from Rachel Klukovich, PhD, of Excerpta Medica, funded by Bristol Myers Squibb. All authors contributed to the conception and interpretation of this manuscript; they are fully responsible for all content and editorial decisions.
Declarations
Funding
This work was supported by Bristol Myers Squibb. Bristol Myers Squibb provided funding for study and medical writing support. Publication of the study was not contingent on Bristol Myers Squibb’s approval or censorship of the manuscript.
Conflicts of Interest
Kyna Gooden, Derek Tang, and Samantha Slaff report employment by and stock ownership in Bristol Myers Squibb. Yu-Hsuan Shih reports travel support and employment from Bristol Myers Squibb, stock ownership in Novartis, and support for study materials and medical writing from the study team from University of Utah. Nathorn Chaiyakunapruk reports research funding from Bristol Myers Squibb. Chia Jie Tan is an Editorial Board Member of PharmacoEconomics. He was not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions. All other authors have stated that they do not have any conflicts of interest regarding the content of this article.
Ethics Approval
The University of Utah Institutional Review Board (IRB) approved this study (IRB-00147477) to be exempt from human subject review.
All authors conceptualized and designed the study and critically revised the manuscript for important intellectual content. Syeed, Tan, Ngorsuraches, and Chaiyakunapruk acquired the data; Syeed, Tan, Gooden, Ngorsuraches, and Chaiyakunapruk analyzed and interpreted the data; Syeed, Tan, Ngorsuraches, and Chaiyakunapruk drafted the manuscript; Syeed, Tan, and Ngorsuraches performed statistical analysis and provision of study materials; Chaiyakunapruk acquired funding; Syeed, Tan, and Ngorsuraches provided administrative, technical, or logistic support; and Gooden, Tang, Ngorsuraches, and Chaiyakunapruk provided supervision. All authors read and approved the final manuscript.
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